Spring Boot

Cloud-native modernization or death? A false dichotomy

Cloud-native modernization or death? A false dichotomy

DevNation Tech Talks are hosted by the Red Hat technologists who create our products. These sessions include real solutions plus code and sample projects to help you get started. In this talk, you’ll learn about cloud-native modernization from Daniel Oh and Burr Sutter.

Are you familiar with the tight coupling of applications with their underlying platform that makes change hard? Or, coupling that creates a lack of scalability, performance, and flexibility for existing applications built with legacy technology? How about the fact that re-architecting applications cannot be done overnight?

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vscode-xml 0.14.0: A more customizable XML extension for VS Code

vscode-xml 0.14.0: A more customizable XML extension for VS Code

Red Hat’s XML extension for Visual Studio Code (VS Code) has improved significantly since the last release. This article is an overview of the most notable updates in the vscode-xml extension 0.14.0 release. Improvements include embedded settings documentation, customizable document outlines, links for seamless XML catalog navigation, and error aggregation for schema validation.

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Build a data streaming pipeline using Kafka Streams and Quarkus

Build a data streaming pipeline using Kafka Streams and Quarkus

In typical data warehousing systems, data is first accumulated and then processed. But with the advent of new technologies, it is now possible to process data as and when it arrives. We call this real-time data processing. In real-time processing, data streams through pipelines; i.e., moving from one system to another. Data gets generated from static sources (like databases) or real-time systems (like transactional applications), and then gets filtered, transformed, and finally stored in a database or pushed to several other systems for further processing. The other systems can then follow the same cycle—i.e., filter, transform, store, or push to other systems.

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Using Spring Cloud Functions with OpenShift Serverless

Using Spring Cloud Functions with OpenShift Serverless

Spring Cloud Functions are yet another interesting option for Java developers when building serverless applications. You have already seen how to build and run applications for Red Hat OpenShift Serverless using Quarkus, but in this article, we talk about how to use Spring Cloud Functions and walk you through those steps. These steps are similar to running any other Spring Boot application with OpenShift Serverless. One of the benefits of building an open hybrid serverless platform is giving developers a choice of programming languages, tools, frameworks, and portability across any environment to run serverless applications. Beyond that, you want to ensure that the developer experience and overall workflow is intuitive and practical, which is what you will learn here.

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Red Hat Runtimes brings Vert.x and Dekorate to Spring Boot 2.2.6

Red Hat Runtimes brings Vert.x and Dekorate to Spring Boot 2.2.6

The latest update to Red Hat Runtimes features support for Spring Boot 2.2.6, along with the Dekorate project and Spring Reactive. Together, these technologies are a boost for developers building Spring-based applications on the Red Hat OpenShift Container Platform. In this article, I present the highlights of this update.

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Adding keystores and truststores to microservices in Red Hat OpenShift

Adding keystores and truststores to microservices in Red Hat OpenShift

You might not need Secure Socket Layer (SSL)-based communication between microservices in the same cluster, but it’s often a requirement if you want to connect to a remote web service or message broker. In cases where you will expose a web service or other endpoints, you might also have to use a custom keystore in a microservice deployed on Red Hat OpenShift, so that external clients only connect with a specific truststore.

In this article, I show you how to configure a keystore and a truststore for a Java-based microservice built with Spring Boot. I used the Apache Camel and CXF libraries from Red Hat Fuse to develop the microservice. I used a source-to-image (S2I) deployment and tested the examples in Red Hat OpenShift 4.3.

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Change data capture with Debezium: A simple how-to, Part 1

Change data capture with Debezium: A simple how-to, Part 1

One question always comes up as organizations moving towards being cloud-native, twelve-factor, and stateless: How do you get an organization’s data to these new applications? There are many different patterns out there, but one pattern we will look at today is change data capture. This post is a simple how-to on how to build out a change data capture solution using Debezium within an OpenShift environment. Future posts will also add to this and add additional capabilities.

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